Buffer Optimization

require(tidyverse)

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require(flowCore)

Loading required package: flowCore

require(flowClust)

Loading required package: flowClust

Attaching package: ‘flowClust’

The following object is masked from ‘package:graphics’:

    box

The following object is masked from ‘package:base’:

    Map

require(openCyto)

Loading required package: openCyto

require(ggcyto)

Loading required package: ggcyto
Loading required package: ncdfFlow
Loading required package: BH
Loading required package: flowWorkspace
As part of improvements to flowWorkspace, some behavior of
GatingSet objects has changed. For details, please read the section
titled "The cytoframe and cytoset classes" in the package vignette:

  vignette("flowWorkspace-Introduction", "flowWorkspace")

require(cowplot)

Loading required package: cowplot

Attaching package: ‘cowplot’

The following object is masked from ‘package:lubridate’:

    stamp

require(ggrdiges)

Loading required package: ggrdiges
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘ggrdiges’

old <- theme_set(theme_minimal())

Background

We previously observed a “dead-like” population in mock-treated samples, and also an unstained population in 10 mM H2O2-treated cells. Here, we explore different staining buffer compositions and monitor those two behaviors.

Data

This is the flow cytometry data for mig-log phase C. glabrata cells stained with PI/SYTO9/PI+SYTO9 in different staining buffers, and run through flow cytometry (details in ELN).

Import data

FCS files are stored in RDSS/user/flow cytometry, FCS is read and write into an input tsv table. The tsv file is avaliable from the Input folder.

# use relative path to make it easier for collaboration
data.path = "../../../00-Shared/02-data/02.01-flow-cytometry/20240108-extended-buffer-optimization/"
dat.f1e <- read.flowSet(path = data.path,
                        transformation = FALSE,        # the original values are already linearized. 
                        emptyValue = FALSE,
                        alter.names = TRUE,            # change parameter names to R format
                        column.pattern = ".H|FSC|SSC") # only load the height variables for the fluorescent parameters

Simplify the sample names

Gatting strategies

The following gaphing steps are used to gate singlets by FSC and SSC values. Only singlets are included in analysis.

Gate for singlets

Gate for outlier

outlier.gate <- rectangleGate(filterId = "-outlier", "FSC.H" = c(1.2e5, 1e6), "SSC.H" = c(1e2, 1e6))
ggcyto(dat[13], aes(x = FSC.H, y = SSC.H), subset = "root") +
  geom_hex(bins = 64) + geom_gate(outlier.gate) + facet_wrap(~name, ncol = 2) + ggcyto_par_set(limits = "instrument")

Coordinate system already present. Adding new coordinate system, which will replace the existing
one.

Add gate to GS

# create a GatingSet
gs <- GatingSet(dat)
# add root gate
gs_pop_add(gs, outlier.gate, parent = "root")

[1] 2

recompute(gs)

done!

Gate for singlets

scPars <- ggcyto_par_set(limits = list(x = c(0,1e6), y = c(30,300)))
ex <- Subset(dat[c(5, 15)], outlier.gate)
polygon <- matrix(c(1e5, 1e5, 1e6, 1e6, 
                    60, 105, 135,60), ncol = 2)
colnames(polygon) <- c("FSC.H", "FSC.W")
singlet.gate <- polygonGate(filterId = "singlet", .gate = polygon)
ggcyto(ex, aes(x = FSC.H, y = FSC.W)) + geom_hex(bins = 128) + geom_gate(singlet.gate) + geom_stats() + scPars

Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Add this gate to the gatingSet

gs_pop_add(gs, singlet.gate, parent = "-outlier", name = "singlet")

[1] 3

recompute(gs)

done!

Gate for the unstained population

Unstained population is defined as population below 10^2 in both channels (in the noise range).

#scPars <- ggcyto_par_set(limits = list(x = c(0,1e6), y = c(0,1e6)))
select <- c(7,19)
ex <- Subset(dat[select], singlet.gate)
polygon2 <- matrix(c(0, 1e2, 1e2, 0,
                    0, 0, 1e2, 1e2), ncol = 2)
colnames(polygon2) <- c("BL1.H", "BL3.H")
unstained.gate <- polygonGate(filterId = "unstained", .gate = polygon2)
ggcyto(ex, aes(x = BL1.H, y = BL3.H)) +
  geom_hex(bins = 64) +
  geom_gate(unstained.gate) + geom_stats() +# scPars +
  scale_x_logicle() + scale_y_logicle() 

Add this gate to the gatingSet

Gate for the dead-like population

Dead population is defined based on 1M treated sample.

#scPars <- ggcyto_par_set(limits = list(x = c(0,1e6), y = c(0,1e6)))
select <- c(19,22)
ex <- Subset(dat[select], singlet.gate)
#polygon <- matrix(c(10^3, 10^2.5,10^3, 10^5,
#                    10^4.5, 10^2.2, 10^2.2, 10^4), ncol = 2)
polygon <- matrix(c(10^2, 10^3, 10^4.5, 10^2.5,
                    10^2.3, 10^2.3, 10^4, 10^4), ncol = 2)
colnames(polygon) <- c("BL1.H", "BL3.H")
dead.gate <- polygonGate(filterId = "dead_like", .gate = polygon)
ggcyto(ex, aes(x = BL1.H, y = BL3.H)) +
  geom_hex(bins = 64) +
  geom_gate(dead.gate) + geom_stats() + #scPars +
  scale_x_logicle() + scale_y_logicle() 

Add this gate to the gatingSet

Analysis & Plots

Visualize one set of data

With gates

subset.to.plot <- with(pData(gs), dye == "Both" & date == '2024-01-09')
p <- ggcyto(gs[subset.to.plot],
            aes(x = BL1.H, y = BL3.H), subset = "singlet") + 
  geom_hex(aes(fill = after_stat(density)), bins = 64) + 
  geom_gate("dead_like", linewidth = 0.5, alpha = 0.5) +
  geom_stats(type = "percent", location = "gate", adjust = c(0, 1),
             digits = 1, size = 3) +
  geom_gate("unstained", linewidth = 0.5, alpha = 0.5) +
  geom_stats(type = "percent", location = "data", adjust = c(0.2, 0.003),
             digits = 1, size = 3) +
  facet_grid(buffer ~ treatment,
             labeller = as_labeller(c(tr.levels, bu.levels), multi_line = F)) +
  scale_x_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  scale_y_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  theme_minimal(base_size = 14) + 
  panel_border(color = "gray20") + #background_grid(major = "none", minor = "none") +
  theme(
    #axis.line = element_blank(),
    strip.text.y = element_text(face = 2),
    strip.text.x = element_text(face = 2),
    axis.text = element_text(size = rel(0.6)),
    plot.title = element_blank(),
    legend.position = "none",
    axis.title = element_blank()
  )
print(p)
ggsave("../output/fig2-compare-staining-buffers-with-gate-20240923.png", width = 5, height = 5)

Without gates

subset.to.plot <- with(pData(gs), dye == "Both" & date == '2024-01-09')
p <- ggcyto(gs[subset.to.plot],
            aes(x = BL1.H, y = BL3.H), subset = "singlet") + 
  geom_hex(aes(fill = after_stat(density)), bins = 64) + 
  facet_grid(buffer ~ treatment,
             labeller = as_labeller(c(tr.levels, bu.levels), multi_line = F)) +
  scale_x_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  scale_y_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  theme_minimal(base_size = 14) + 
  panel_border(color = "gray20") + #background_grid(major = "none", minor = "none") +
  theme(
    #axis.line = element_blank(),
    strip.text.y = element_text(face = 2),
    strip.text.x = element_text(face = 2),
    axis.text = element_text(size = rel(0.6)),
    plot.title = element_blank(),
    legend.position = "none",
    axis.title = element_blank()
  )
print(p)
ggsave("../output/fig2-compare-staining-buffers-no-gate-20240923.png", width = 5, height = 5)

Export gated event counts

tmp <- gs_pop_get_stats(gs) %>% 
  mutate(pop = gsub(".*/", "", pop), pop = gsub("-outlier", "cells", pop)) %>% 
  pivot_wider(names_from = pop, names_prefix = "n_", values_from = count) %>% 
  mutate(
    pc_dead_like = num(n_dead_like / n_singlet, label = "%", scale = 100),
    pc_unstained = num(n_unstained / n_singlet, label = "%", scale = 100)
  )

gated_stats <- left_join(
  as_tibble(pData(gs)),
  tmp,
  by = c("name" = "sample")
) %>% dplyr::filter(dye == "Both") %>% select(-dye)
  
print(gated_stats)

write_tsv(gated_stats, file = "../output/fig2-compare-staining-buffer-gated-stats.tsv")

Plot percentages

We will primarily look at the mock sample, as the purpose of the buffer comparison is to find a buffer that will minimize the dead-like and unstained populations.

p1 <- gated_stats %>% 
  # plot the mock sample only
  dplyr::filter(treatment == "0") %>% 
  pivot_longer(
    cols = starts_with("pc_"),
    names_to = "var",
    values_to = "value"
  ) %>% 
  ggplot(aes(x = buffer, y = value))  + 
  geom_bar(stat = "summary", fun = "mean", fill = "gray80") +
  geom_point(size = 3, position = position_jitter(0.05)) + 
  scale_y_continuous(labels = scales::percent_format()) +
  scale_x_discrete(labels = bu.levels) +
  facet_wrap(~var, nrow = 2, scales = "free_y", labeller = as_labeller(
    c("pc_dead_like" = "% Dead-like", "pc_unstained" = "% Unstained"))) +
  labs(x = NULL, y = "Population frequency") +
  theme_minimal(base_size = 18) + panel_border(color = "gray20") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        strip.text = element_text(color = "steelblue", face = 2))
print(p1 + facet_wrap(~var, ncol = 2, scales = "free_y", labeller = as_labeller(
    c("pc_dead_like" = "% Dead-like", "pc_unstained" = "% Unstained"))))
ggsave(filename = "../output/fig2bc-compare-buffer-plot-stats.png", 
       plot = p1, width = 4, height = 7)

Sample-to-sample variation

Visaulize sample-to-sample variability in mock-treated cells across three replicates

subset.to.plot <- with(pData(gs), dye == "Both" & treatment == "0")
date.levels = paste("Replicate", 1:3); names(date.levels) = unique(sample$date)
p <- ggcyto(gs[subset.to.plot],
            aes(x = BL1.H, y = BL3.H), subset = "singlet") + 
  geom_hex(aes(fill = after_stat(density)), bins = 64) + 
  facet_grid(date~buffer, labeller = as_labeller(c(date.levels, bu.levels), 
                                                   multi_line = F)) +
  scale_x_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  scale_y_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  theme_minimal(base_size = 14) + 
  panel_border(color = "gray20") + #background_grid(major = "none", minor = "none") +
  theme(
    #axis.line = element_blank(),
    strip.text.y = element_text(face = 2),
    strip.text.x = element_text(face = 2),
    axis.text = element_text(size = rel(0.6)),
    plot.title = element_blank(),
    legend.position = "none",
    axis.title = element_blank()
  )
print(p)

Supplementary plots

PI-alone

subset.to.plot <- with(pData(gs), dye == "PI" & date == '2024-01-09')
p <- ggcyto(gs[subset.to.plot],
            aes(x = BL1.H, y = BL3.H), subset = "singlet") + 
  geom_hex(aes(fill = after_stat(density)), bins = 64) + 
  facet_grid(buffer ~ treatment,
             labeller = as_labeller(c(tr.levels, bu.levels), multi_line = F)) +
  scale_x_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  scale_y_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  theme_minimal(base_size = 14) + 
  panel_border(color = "gray20") + #background_grid(major = "none", minor = "none") +
  theme(
    #axis.line = element_blank(),
    strip.text.y = element_text(face = 2),
    strip.text.x = element_text(face = 2),
    axis.text = element_text(size = rel(0.6)),
    plot.title = element_blank(),
    legend.position = "none",
    axis.title = element_blank()
  )
print(p)
ggsave("../output/fig2sup-compare-staining-buffers-PI-only-20240923.png", width = 5, height = 5)

SYTO9 alone

subset.to.plot <- with(pData(gs), dye == "SYTO9" & date == '2024-01-09')
p <- ggcyto(gs[subset.to.plot],
            aes(x = BL1.H, y = BL3.H), subset = "singlet") + 
  geom_hex(aes(fill = after_stat(density)), bins = 128) + 
  facet_grid(buffer ~ treatment,
             labeller = as_labeller(c(tr.levels, bu.levels), multi_line = F)) +
  scale_x_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  scale_y_logicle(breaks = c(10^2, 10^3, 10^4)) + 
  theme_minimal(base_size = 14) + 
  panel_border(color = "gray20") + #background_grid(major = "none", minor = "none") +
  theme(
    #axis.line = element_blank(),
    strip.text.y = element_text(face = 2),
    strip.text.x = element_text(face = 2),
    axis.text = element_text(size = rel(0.6)),
    plot.title = element_blank(),
    legend.position = "none",
    axis.title = element_blank()
  )
print(p)
ggsave("../output/fig2sup-compare-staining-buffers-SYTO9-only-20240923.png", width = 5, height = 5)